Data analysis and predictive systems and related methodologies

ABSTRACT

A method, computer system, and computer memory medium optimizing a transductive model Mx suitable for use in data analysis and for determining a prognostic outcome specific to a particular subject are disclosed. The particular subject may be represented by an input vector, which includes a number of variable features in relation to a scenario of interest. Samples from a global dataset D also having the same features relating to the scenario and for which the outcome is known are determined. In an embodiment, a subset of the variable features within a neighborhood formed by the samples are ranked in order of importance to an outcome. The prognostic transductive model is then created based, at least in part, on the subset, the ranking, and the neighborhood. The subset and the neighborhood are then optimized until the accuracy of the transductive model is maximized.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. application Ser.No. 13/088,306, filed Apr. 15, 2011, which is a continuationapplication, and claims the benefit under 35 U.S.C. §§120 and 365 of PCTApplication No. PCT/NZ2009/000222, filed on Oct. 15, 2009, which arehereby incorporated by reference. PCT/NZ2009/000222 also claimedpriority from New Zealand Patent Application No. 572036, filed on Oct.15, 2008, which is hereby incorporated by reference. PCT/NZ2009/000222also claimed priority from U.S. Patent Application No. 61/105,742, filedon Oct. 15, 2008, which is hereby incorporated by reference.

BACKGROUND

1. Field

The described technology relates to data analysis and predictive systemsand related methodologies. In particular the described technologyrelates to customised or personalised data analysis and predictivesystems and related methodologies.

2. Description of Related Technology

The concept of personalised medicine has been promoted widely in therecent years through the collection of personalised databases,establishment of new journals and new societies and publications ininternational journals (see for example ref. 1-7). Despite the furore ofinterest in this area, there are at present no adequate data analysismethods and systems which can produce highly accurate and informativepersonalised models from data.

Contemporary medical and other data analysis and decision supportsystems use predominantly inductive global models for the prediction ofa person's risk, or likely outcome of a disease for an individual. InUS20050131847A1, for example, features are pre-processed to minimiseclassification error in a global Support Vector Machine model used toidentify patterns in large databases. Pre-processing may be performed toconstrain features used to train the global SVM learning system. Globalmodelling in general is concerned with deriving a global formula (e.g.via regression, a “black box neural network”, or a support vectormachine) from the personal data of many people. The global formula isexpected to perform well on any new subject, at any time, anywhere inworld. Based on this expectation, drugs may be designed to target adisease, and these drugs are assumed to be useful for everybody whosuffers from this disease. When a global model is created, a set offeatures (variables) may usually be selected that applies to the wholeproblem space (e.g., all samples in the available data). However,statistics have shown very clearly that drugs developed by such globalmodels will only be effective for around average of 70% of people inneed of treatment, leaving a relatively large number of patients whowill not benefit at all from treatment with the drug. With aggressivediseases such as cancer, any time wasted, e.g. either a patient notbeing treated, or being treated, with an ineffective treatment, can bethe difference between life and death. In particular, it would be usefulto determine from a sample taken from a patient (e.g. blood sample,tissue, clinical data and/or DNA) into what category a patient falls.This information can also be used to determine and develop treatmentsthat will be effective at treating the remainder of the population.

It would therefore be useful if there could be provided data analysismethodologies and systems, which based on available population data, arecapable of creating models which are more useful and informative foranalysing and/or assessing an individual person for a given problem.Such models should also ideally achieve a higher degree of accuracy ofprediction of outcome or classification than conventional systems andmethodologies.

A step towards personalised medicine and profiling may be the creationof global models, that cover a whole population of data, but importantlycomprise many local models, each of them covering a cluster(neighbourhood) of similar data samples (vectors) Such models are calledlocal learning models. Such models may be adaptive to new data. Oncecreated, a person's information can be submitted and a personal profileextracted in terms of the closest local model which may be based on theneighbourhood of vectors in the dataset closest to that of subjectperson. Such models include evolving connectionist systems (EGOS), suchas those previously developed, patented and published (Kasabov 2000,2002 and 2007). These methods identify groups (clusters orneighbourhoods) of similar samples and develop a local model for eachcluster through a machine learning algorithm, collectively all clusterscover the whole problem space. While local learning models have beenvery useful to adapt to new data and discover local information andknowledge, these methods do not select specific subsets of features andprecise neighbourhood of samples for a specific individual that would berequired for a true personalised modeling, for example in personalisedmedicine.

While inductive modeling results in the incremental creation of a globalmodel where new, unlabeled data may be “mapped” through a recallprocedure, transductive inference methods (transductive models) estimatethe value of a potential model (function) only in a single point of thespace (e.g., that of the new data vector) and utilise the information(features) of samples close in space (e.g., related to this point). Thisapproach seems to be more appropriate for clinical and medicalapplications, where the focus may be not so much on the model, but moreon the individual patient. The focus may be on the accuracy ofprediction for any individual patient as opposed to the global errorassociated with a global model which merely highlights the shortcomingsof an inductive approach. Thus, with a transductive approach eachindividual data vector (e.g. a patient in any given medical area)obtains a customised, local model, that best fits the new data, ratherthan a global model, where new data may be matched to a model (formula)averaged for the whole dataset which fails to take into account specificinformation peculiar to individual data samples. Thus a transductiveapproach seems to be a step in the right direction when looking todevise personalized modelling useful in personalized medicine.

The general principle of transductive modeling can be stated as thefollowing: for every new input vector x, that needs to be processed fora classification or a prognostic task, the closest K samples, that forma new sub-data set Dx, may be derived from an existing global data setD. A new model Mx may be dynamically created from these samples. Thesystem may then be used to calculate the output value y for this inputvector x (Vapnik 1998).

A simple and classical transductive inference method may be theK-nearest neighbour method (K-NN) where the output value y for a newvector x may be calculated as the average of the output values of theK-nearest samples from the data set Dx. In a weighted K-NN method (WKNN)the output y may be calculated based on the weighted distance of theK-NN samples to x:y=(Σ_(j=1,K)(w _(j) y _(j)))/(Σ_(j=1,K)(w _(j)))  (1)where: y is the output value for the sample x from Dx; y_(j) is theoutput value for the sample x_(j) in the neighbourhood of x; w_(j) isthe weighted distance between x and x_(j) measured as:w _(j)=max(d)−[d _(j)−min(d)].  (2)

In Eq. (2), the vector distance d=[d₁, d₂, . . . d_(K)] may be definedas the distances between the new input vector x and the nearest samples(x_(j), y₁) for j=1 to K; max(d) and min(d) are the maximum and minimumvalues in d respectively.

In general, distance between two q—element vectors x and z of samevariables may be measured as normalised Euclidean distance defined asfollows:d _(x,z)=SQRT(Σ_(l=1 to q)(x _(l) −z _(l))²)/q  (3)

In another classification method, called WWKNN, not only may the nearestsamples be weighted based on their distance to the new sample x, but thecontribution of each of the variables may be weighted based on theirimportance for the nearest neighbor area of x (Kasabov 2007).

The KNN, WKNN and WWKNN methods use a single formula to calculate theoutput y for the input vector x based on the K nearest neighbours. Thesemethods do not suggest how to select the number K and the most suitableset of K nearest samples, neither they suggest how to select the numberof variables V, that would give the best accuracy of each personalisedmodel Mx. By way of contrast these methods use a fixed number of Knearest neighbours and a fixed number of variables.

Other methods create a machine learning model from the K nearestneighbours and the model may then be used to calculate the output y.Such methods for example are: Transductive Neural Fuzzy InferenceSystem—NFI and Transductive Neural Fuzzy Inference System with WeightedData Normalization—TWNFI (Song and Kasabov 2006). As the above group ofmethods, these methods do not suggest how to select the number K ofnearest samples, neither they suggest how to select the number ofvariables V, that would give the best accuracy of the personalised modelMx.

To summarise, in the above transductive methods, there is no efficientmethod for personalised feature selection (e.g. features such asimportant genes, clinical and/or other variables) required forpersonalised prognosis, classification, profiling, and/or treatmentselection. These transductive methods also do not rank variables(features) in terms of importance for a person and for an optimalpersonal model creation based on these variables and a personalisedselection of the nearest neighbour samples from the available data set.There is also no methodology to suggest how individual scenarios forpersonal improvement (e.g. treatment) can be designed.

SUMMARY OF CERTAIN INVENTIVE ASPECTS

According to an aspect of the described technology a computerimplemented method of optimising a model Mx suitable for use in dataanalysis and determining a prognostic outcome specific to a particularsubject (input vector x), the subject comprising a number of variablefeatures in relation to a scenario of interest for which there is aglobal dataset D of samples also having the same features relating tothe scenario, and for which the outcome is known is provided; the methodcomprising:

-   a) determining what number and which variables (features) Vx will be    used in assessing the outcome for the input vector x;-   b) determining what number Kx of samples from within the global data    set D will form a neighbourhood about x;-   c) selecting Kx samples from the global data set which have the    variable features that most closely accord to the variable features    of the particular subject x to form the neighbourhood Dx;-   d) ranking the Vx variable features within the neighbourhood Dx in    order of importance to the outcome of vector x and obtaining a    weight vector Wx for all variable features Vx;-   e) creating a prognostic model Mx, having a set of model parameters    Px and the other parameters from elements a)-d);-   f) testing the accuracy of the model Mx at element e) for each    sample from Dx;-   g) storing both the accuracy from element f), and the model    parameters developed in elements a) to e);-   h) repeating elements a) and/or b) whilst applying an optimisation    procedure to optimise Vx and/or Kx, to determine their optimal    values and the corresponding sets, before repeating elements c)-h)    until maximum accuracy at element f) is achieved.

According to another aspect of the described technology a method asdescribed above is provided which includes:

-   i) calculating the outcome y for the input vector x using the    optimised model Mx created at element h).

According another aspect of the described technology a methodsubstantially as described above is provided which includes:

-   j) profiling input vector x and comparing important variable    features against important variable features associated with a    desired outcome to provide for, or assist with, development of,    scenarios for improvement of the outcome for input vector x.

According another aspect of the described technology a computerimplemented method of determining a profile of a subject (input vectorx) based on a model Mx and for recommending changes to the profile inrelation to a scenario of interest in order to improve the outcome forinput vector x is provided comprising:

-   (I) creating a personalised profile of input vector x;-   (II) comparing each important variable feature of input vector x to    the average value of each important variable feature of samples    having the desired outcome; and-   (III) determining which important variable features of input vector    x can be altered in order to improve the outcome.

It is desirable in certain embodiments that the determination of whichvariables should be changed will take into account the weight vector Wxof the variable. It is desirable in certain embodiments that thevariables that will be changed will be those which may be important withrespect to the outcome.

The term ‘personalised profile’ as used herein refers to an input vectorand to the predicted outcome for that vector.

According another aspect of the described technology a system isprovided which includes:

-   -   a processor and associated memory (herein collectively the        hardware); the system characterized in that the hardware has        been programmed to:    -   access a global dataset of samples relating to a scenario of        interest, and for which the outcome is known, each sample having        a number of variable features, which may or may not relate to        the scenario;    -   receive input information relating to an input vector x; and    -   to perform a method substantially as described above.

A computer memory medium which contains a program which is capable ofperforming a method as described above on a global dataset of samplesfor which the outcome is known relating to a scenario of interest, eachsample having a number of variable features, which may or may not relateto the scenario; and wherein the program provides for an user interfaceto receive input information relating to an input vector x and whereinthe program also provides for graphic display of the method results.

An embodiment of the proposed method and system of optimising a model Mxsuitable for use in data analysis and determining a prognostic outcomemay include the following modules as shown in FIG. 1):

-   -   Module for most relevant features (variables) Vx selection and        their ranking Wx by importance for x;    -   Module for the selection of a number Kx of neighbouring samples        of x and for the selection of neighbouring samples Dx;    -   Module for a prognostic model Mx creation, defined by the model        parameters Px and the parameters including Kx, Vx, Dx which were        derived in the previous modules;    -   Module for a final output y calculation for x, for personalised        profiling; and    -   Module for the design of scenarios for improvement.

The described technology has utility in relation to a wide variety ofscenarios of interest in areas as diverse as meteorology, drugdevelopment, bioinformatics, personalized medicine, psychologicalprofiling, nutri-genomics, finance and economics, to name but a few.

For ease of reference only the described technology will now bediscussed in relation to personalized medicine, however, this should notbe seen as limiting.

The variable features, also referred to as simply variables or features,may be any piece(s) of information that one has collected in relation tosamples forming a global dataset relating to a scenario of interest. Inpersonalized medicine applications of the described technology thevariable features may relate to the different genes of patientsimplicated in a disease or disorder, clinical data, age, gender. In factthe variable features may be almost information that has been collectedfrom or about the patients in the dataset which may be of relevance tothe disease of interest.

We assume that the scenario of interest (e.g., the problem which is tobe analysed) is either:

-   -   Classification—For simplicity we can assume two classes of        outcome (e.g., output values) for an input vector (e.g., class 1        (survive a disease after treatment), and class 2 (die of disease        after treatment)). However, it will be appreciated that the        described technology is also applicable to multiple class        classification problems; or    -   Risk evaluation and prediction—where the output values assigned        to the samples are continuous values. For example: renal        function evaluation measured as GFR; cardio-vascular risk        measured in probability of an event to happen; for example the        risk of diabetes.

The described technology assumes that there is a global data set D (ormultiple data sets) of individual records (samples) Si=(x_(i),y_(i)),i=1, 2, . . . , N. There is also a new input vector x for which anoutput value (outcome) y is to be calculated.

The variables in the data set D partially or preferably fully overlapwith the variables in the new input vector x. If it is a partialoverlap, a common variable set of q variables in D and x is determinedand a new data set is created as a subset of D. Initially, it is assumedthat all variables have equal absolute and relative importance for x inrelation to predicting its unknown output y:w _(v1) =w _(v2) = . . . =w _(vq)=1;  (4)andw _(v1,norm) =w _(v2,norm) = . . . =W _(vq,norm)=1/q;  (5)

The numbers initially determined for Vx and Kx at steps a) and b) may bedetermined in a variety of different ways without departing from thescope of the present invention.

In preferred embodiments the number for Vx (Step a) and/or Kx (Step b)may be initially determined (e.g., prior to iteration of the methodsteps as per step h)) following an assessment of the global dataset interms of size and/or distribution of the data. Minimum and maximumvalues of these parameters may also be established a priori based on thedata available and the problem. For example, Vx_min=3 (minimum threevariables used in a personalised model) and Vx_max<Kx (the maximumvariables used in a personalized model is not larger than the number ofsamples in the neighbourhood Dx of x), usually Vx_max<20. The initialset of variables may include expert knowledge, e.g., variables which arereferenced in the literature as highly correlated to the outcome of theproblem (disease) in a general sense (over the whole population). Suchvariables are the BRCA genes, when the problem is predicting outcome ofbreast cancer (van Veer et al, 2002). For an individual patient the BRCAgenes may interact with some other genes, which interaction will bespecific for the person or a group of persons and may be likely to bediscovered through local or/and personalized modeling only (Kasabov etal, 2005).

The present invention, when compared with global or local modeling, maystart the modeling process with all relevant variables available for aperson, rather than with a fixed set of variables in a global model thatmay well be statistically representative for a whole population, but notnecessarily representative for a single person in terms of optimal modeland best profiling and prognosis for this person.

Selecting the initial number for Kx and also the minimum and the maximumnumbers Kx_min and Kx_max will also depend on the data available and onthe problem in hand. A general requirement is that Kx_min>Vx, and,Kx_max<cN, where c is for example 0.5. Several formulas have beenalready suggested and experimented (Vapnik, 1998; Mohan and Kasabov,2005), for example:

-   -   Kx_min equals the number of samples that belong to the class        with smaller number of samples when the data is imbalanced (one        class has many more samples, e.g. 90%, than the another class)        and the available data set D is of small or medium size (e,g.,        hundreds to few thousands samples);    -   Kx_min=SQRT (N), where N is the total number of samples in the        data set D.

A subsequent iterations of method steps a) and b) the Vx and Kxparameters may be optimized pursuant to step h) via an optimizationprocedure such as is outlined further below.

At step h) the optimization procedure(s) which can be employed with themethod and system of the described technology may include threealternative methods and/or a combination of the methods set out below:

-   1. An exhaustive search, where all or some possible values of the    all or some of the parameters Vx, Wx, Kx, Mx and Px (see FIG. 2)    within their constraints, are used in their combination and the    model Mx with the best accuracy is selected.-   2. A genetic algorithm (GA) may be used (Goldberg 1989) to optimize    all or some parameters from the “chromosome” (FIG. 2).    -   Genetic algorithms (GA) are methods that have been used to solve        complex combinatorial and organizational problems with many        variants, by employing analogy with Nature's evolution. Genetic        algorithms were introduced for the first time in the work of        John Holland (Holland 1975). They were further developed by him        and other researchers (Goldberg 1989).    -   The most important terms used in a GA are analogous to the terms        used in biology in relation to the study of Genetics. They are:        -   gene—a basic unit, which defines a certain characteristic            (property) of an individual. In case of FIG. 2, “genes” are            the parameters and variables to be optimized for a            personalized model Mx.        -   chromosome—a string of genes; it is used to represent an            individual, or a possible solution to a problem in the            solution space (see FIG. 2).        -   population—a collection of individuals—in our case it is a            population of chromosomes each one representing one            personalised model for the new input vector x.        -   crossover (mating) operation—a set of different models is            taken and a new set of models is produced, e.g. from two            models, each represented by a chromosome, new ones are            generated through combining parts of the first model            chromosome (mother) and parts from the other (father).        -   mutation—random change of a gene (variable) in a chromosome.        -   fitness (goodness) function—a criterion which evaluates how            good each individual is. In our case it will be the accuracy            Ax of the model (or the error Ex).        -   selection—a procedure of choosing a part of the population            which will continue the process of searching for the best            solution, e.g. the 10 best models.    -   The steps in a GA are:        -   Generate initial population of individuals (personalised            models)—each individual defined as a chromosome containing            parameters—genes (This is done in Steps a and b as explained            in the invention).        -   Evaluate the fitness of each individual (the accuracy of            each model) using a fitness function (accuracy of the model            Ax). In our case this is done in Step f.        -   Select a subset of individuals based on their fitness (This            is done is Step h.        -   Apply a crossover procedure on the selected individuals to            create a new generation of a population h        -   Apply mutation h        -   Continue with the previous procedure h until a desired            solution (with a desired fitness) is obtained, or the run            time is over.    -   Genetic algorithms comprise a great deal of parallelism. Thus,        each of the branches of the search tree for best individuals can        be utilized in parallel with the others. This allows for an easy        realization of the genetic algorithms on parallel architectures.    -   Selection of the best models to continue the process of        optimization is based on fitness. A common approach is        proportional fitness (roulette wheel selection), e.g., if a        model Mx is twice as good as another one, its probability of        being selected for the crossover process is twice higher.        Roulette wheel selection gives chances to individuals according        to their fitness evaluation (see example below (from Kasabov,        2007).    -   Important feature of the selection procedure is that fitter        individuals (models Mx with higher accuracy) are more likely to        be selected.    -   The selection procedure can involve also keeping the best        individuals from the previous generation. This operation is        called elitism.    -   After the best individuals are selected from a population of        models, a cross over operation is applied between these        individuals. Different cross-over operations can be used:    -   one-point cross-over;    -   three-point cross over (as shown in FIG. 8 from Kasabov, 2007),        or more.    -   Mutation can be performed in the following ways:    -   For a binary string, just randomly ‘flip’ a bit.    -   For a more complex “genes” and “chromosomes”, randomly select a        gene an change its value.    -   Some GA methods just use mutation (no crossover, e.g.        evolutionary strategies). Normally, however, mutation is used to        search in a “local search space”, by allowing small changes in        the “genotype” (and therefore hopefully in the “phenotype”).    -   In other implementations of the proposed in the invention method        and system other evolutionary computation algorithms can be used        for the optimization of the parameters of a personalized model        (FIGS. 1, 2), such as evolutionary strategies (Kasabov, 2007).    -   While GA have been used in some previously developed methods for        model optimization, e.g.: NeuCom and ECF parameter and feature        optimization for local modeling; model and parameter        optimization of global models (Sureka, 2008); basic parameter        and feature optimization for personalised models (Mohan and        Kasabov, 2005), GA and the other evolutionary optimization        techniques have never been used for the integrated optimization        of features V, feature weights W, number of nearest neighbours        K, models M and their parameters P related to personalised        modeling.

Step c) goes on to find the closest Kx neighboring samples to x from Dand forms a new data set Dx. Preferably, step c) uses a novel distancemeasure which is a local weighted variable distance measure that weighsthe importance of each variable V_(l) (l=1, 2, . . . , q) to theaccuracy of the model outcome calculation for all samples in theneighbourhood Dx using a classification or prediction model. Forexample, the distance between x and another sample z from Dx may bemeasured as a local weighted variable distance:d _(x,z)=SQRT(Σ_(l=v1 to vq)((1−w _(l,norm))(x _(l) −z _(l))²))/q  (6)where: w_(l) is the weight assigned to the variable V_(l) and its valueis calculated as:w _(l,norm) =w _(l)/Σ_(l=1 to q)(w _(l))  (7)

The above formulas (6) and (7) are different from the traditionally usedone (3) and this is the basis of a novel supervised neighbourhoodclustering method proposed here, where the distance between a clustercentre (in our case it is the vector x) and cluster members(neighborhood samples from Dx) is calculated not only based on thegeometrical distance, as it is in the traditional unsupervisedclustering methods, but on the relative importance weight vector Wx forthe output values of all samples in the neighborhood Dx.

After a subset Dx of Vx variables and Kx samples is selected in step c),the variables are ranked at step d) in a descending order of theirimportance for prediction of the output y of the input vector x and aweighting vector Wx obtained. Through an iterative optimizationprocedure explained below the number of the variables Vx to be used foran optimized personalized model Mx will be reduced, selecting only themost appropriate variables that will provide the best personalizedprediction accuracy of the model Mx. For the weighting Wx (e.g.,ranking) of the Vx variables, the following alternative methods can beused:

-   -   (i) In one implementation, applicable to a classification task,        calculate Wx as normalised SNR (Signal-to-Noise Ratio)        coefficients (or another ranking coefficients, such as t-test,        or p-value) and sort the variables in descending order: V1, V2,        . . . , Vv, where: w₁>=w₂>= . . . >=w_(v), calculated as        follows:        w _(l)=abs(M _(l) ^((class 1,x)) −M _(l)        ^((class 2,x)))/(Std_(l) ^((class1))+Std_(l) ^(class2)))  (8)

Here M_(l) ^((class s)) and Std_(l) ^((class s)) are respectively themean value and the standard deviation of variable x_(l) for all vectorsin Dx that belong to class s.

This method is very fast, but evaluates the importance of the variablesin the neighborhood Dx one by one and does not take into account apossible interaction between the variables that might affect the modeloutput.

-   -   (ii) In another implementation, applicable to both        classification and prediction tasks, for all variables Vx all        possible combinations of values of their weights Wx are tested        through an exhaustive search to maximize the overall accuracy of        a model built on the data Dx. For example, each variable weight        w_(i) can take values from 0 to 1 with a step of 0.2. In this        case, the number of the tests will be 6^(Vx), which for small        number of variables is operational but for a large number of        variables is very time consuming and not practical. This is an        exhaustive search method for the optimization of the variable        weights Wx in regard to the model output for all samples from        Dx.    -   (iii) In a third implementation, applicable if the number of        variables prevents using method (ii) above, a faster        optimization method can be used instead of the exhaustive search        of all possible combinations as in (ii). Such method is for        example the GA (as explained above).    -   (iv) In a fourth implementation, another evolutionary        algorithm—quantum inspired evolutionary algorithm, is used to        select the optimal variable set Vx for every new input vector x        and to weigh the variables through probability wave function as        in (Defoin-Platel, Schliebs, Kasabov, 2007 and 2008).

At step e) to create a prognostic model Mx a classification orprediction procedure is applied to the neigbourhood Dx of Kx samples toderive a personalized model Mx using the already defined variables Vx,variable weights and a model parameter set Px. At step f) a localaccuracy error Ex, that estimates the personalised accuracy of thepersonalised prognosis (classification) for the data set Dx using modelMx is evaluated. This error is a local one, calculated in theneighborhood Dx, rather than a global accuracy, that is commonlycalculated for the whole problem space D.

A variety of methods for calculating error can be employed such as.

A novel formula for calculating error which may be utilized in preferredembodiments of the present invention:Ex=(Σ_(j=1,Kx)(1−d _(x,j))E _(j))/Kx  (9)where: d_(x,j) is the weighted Euclidean distance between sample x andsample Sj from Dx that takes into account the variable weights Wx; E_(j)is the error between what the model Mx calculates for the sample j fromDx and what its real output value is, for example: if the model Mxcalculates for the sample SjεDx an output of 0.3 and this is aclassification problem where sample Sj's class is 0, the error will be 0if a classification threshold of 0.5 is used; the error Ej will be 0.2if the desired output for Sj is 0.1 and it is a risk prediction problem.

In the above formula, the closer a sample Sj to x is, based on aweighted distance measure, the higher its contribution to the error Ex.Distant samples from x in Dx do not contribute much to the local errorEx.

The calculated personalized model Mx accuracy at step f) is:Ax=1−Ex  (10)

At step g) the best accuracy model obtained is stored for a futureimprovement and optimization purposes,

It is envisaged that the described technology may employ any number ofdifferent classification or prediction procedures at step e) to createthe model. These may preferably include methods that use weightedvariables for the evaluation of an output value when an input vector ispresented. Two, publicly available methods of this category are:

-   -   The weighted-weighted K-nearest neighbor method, WWKNN (Kasabov,        2007a, b);    -   The transductive, weighted neuro-fuzzy inference method TWNFI        (Song and Kasabov, 2006)    -   but should in no way be restricted to them. Statistical methods,        such as: linear regression method; logistic regression; support        vector machine; nearest neighbour K-NN method; W-KNN method; and        machine learning methods, such as: neural networks; fuzzy        systems; evolving fuzzy neural network EFuNN (Kasabov, 2000,        2002, 2007) can also be used for some specific applications.

At step h) the method iteratively returns to all previous steps toselect another set of parameter values for the parameter vector fromFIG. 2 according to one of the four optimization procedures listed above(exhaustive search, genetic algorithm, quantum evolutionary algorithm, acombination between these three methods) until the model Mx with thebest accuracy is achieved.

The method at step h) may in some preferred embodiments also optimizethe classification procedure that is used by the method at step e) alongwith the parameters Px of said procedure.

The method at step h) may also preferably optimize any parameters Px ofthe classification/prediction procedure. These parameters Px may beoptimized by an optimization procedure substantially as described above.

Once the best model Mx is derived at step h), at step i) an output valuey for the new input vector x is evaluated using this model. For example,when using the WWKNN method, the output value y for the input vector xis calculated using the formula:y=(Σ_(j=1,K)(a _(j) y _(j)))/(Σ_(j=1,K)(w _(j)))  (11)where: y_(j) is the output value for the sample x in the neighbourhoodDx of x and:a _(j)=max(d)−[d _(j)−min(d)].  (12)

In Eq. (16), the vector distance d=[d₁, d₂, . . . d_(K)] is defined asthe distances between the new input vector x and the nearest samples(x_(j), y_(j)) for j=1 to K; max(d) and min(d) are the maximum andminimum values in d respectively. Euclidean distance d_(j) betweenvector x and a neighboring one x_(j) is calculated now as:d _(j)=sqr[sum_(l=1 to V)(w _(l)(x _(l) −x _(jl)))²],  (13)where: w_(l) is the coefficient weighing variable x_(l) in theneighbourhood Dx of x as per Step d).

When using the TWNFI classification or prediction model, the output yfor the input vector x is calculated as follows:

$\begin{matrix}{y = \frac{\sum\limits_{l = 1}^{M}{\frac{n_{l}}{\delta_{l}^{2}}{\prod\limits_{j = 1}^{P}\;{\alpha_{lj}{\exp\left\lbrack {- \frac{{w_{j}^{2}\left( {x_{ij} - m_{lj}} \right)}^{2}}{2\sigma_{lj}^{2}}} \right\rbrack}}}}}{\sum\limits_{l = 1}^{M}{\frac{1}{\delta_{l}^{2}}{\prod\limits_{j = 1}^{P}\;{\alpha_{lj}{\exp\left\lbrack {- \frac{{w_{j}^{2}\left( {x_{ij} - m_{lj}} \right)}^{2}}{2\sigma_{lj}^{2}}} \right\rbrack}}}}}} & (14)\end{matrix}$

Where: M is the number of the closest clusters to the new input vectorx, each cluster l defined as a Gaussian function G_(l) in a Vxdimensional space with a mean value m_(l) as a vector and a standarddeviation δ_(l) as a vector too; x=(x1, x2, . . . , x_(v)); α_(l) (alsoa vector across all variables V) is membership degree to which the inputvector x belongs to the cluster Gaussian function G_(l); n_(l) is aparameter of each cluster (see Song and Kasabov, 2006).

After the output value y for the new input vector x has been calculatedat step i), at step j) a personalized profile Fx of the personrepresented as input vector x is derived, assessed against possibledesired outcomes for the scenario, and possible ways to achieve animproved outcome can be designed.

In one implementation, a personal improvement scenario, consisting ofsuggested changes in the values of the persons' variable features toimprove the outcome for x, according to method steps (I)-(III) below,may be designed as follows:

At step (I) The current person's x profile Fx may be formed as a vector:F _(x) ={Vx,Wx,Kx,Dx,Mx,Px,t},  (15)where the variable t represents the time of the model Mx creation. At afuture time (t+Δt) the person's input data x may change to x′ (due tochanges in variables such as age, weight, gene expression values, etc.),or the data samples in the data set D may be updated and new samplesadded. A new profile Fx′ derived at time (t+Δt) may be different fromthe current one Fx.

At step (II) an average profile Fi for every class Ci in the data Dx(e.g. class 1—good outcome or a desired person's profile, class 2—badoutcome, non-desirable profile) may be created as follows:Fi={(V ₁ _(—) _(av) _(—) _(class) _(—) _(i) ,V ₂ _(—) _(av) _(—)_(class) _(—) _(i) , . . . ,Vv _(—av) _(—) _(class) _(—) _(i)),(w ₁ ,w ₂, . . . ,w _(v))}  (16)where: V _(l) _(—) _(av) _(—) _(class) _(—) _(i)=Σ_(j=1,Nx) _(—)_(class) _(—) _(i)(V _(l) _(—) _(j,i))/N _(x) _(—) _(class) _(—)_(i)  (17)where: V_(l) _(—) _(j,i) is the value of the variable V_(l) for thesample j of class i in the data set Dx of N_(x) _(—) _(class) _(—) _(i)neighbouring samples to x in Dx that belong to class Ci.

The importance of each variable feature is indicated by its weighting.The weighted distance from the person's profile Fx and the average classprofile Fi (for each class i) may be defined as:D(Fx,Fi)=Σ_(l=1,v)abs(V _(lx) −V _(li))·w _(l)  (18)where: w_(l) is the weight for the variable V_(l) in the data set Dx.

Assuming that Fd is the desired profile (e.g. good outcome) the weighteddistance D(Fx,Fd) may be calculated as an aggregated indication of howmuch a person's profile should change to reach the average desiredprofile Fd:D(Fx,Fd)=Σ_(l=1,v)abs(V _(lx) −V _(ld))·w _(l)  (19)

At step (III) a scenario for a person's improvement through changes madeto variable features towards the desired average profile Fd may bedesigned as a vector of required variable changes, defined as:deltaFx,d=(deltaV _(lx,d))_(,for l−1,v) as follows:  (20)deltaV _(lx,d) =V _(lx) −V _(ld), with an importance of: w_(l)  (21)

In any given scenario certain variable features of input vector x willautomatically be, or can be manually, selected as being not capable ofbeing altered in order to improve the outcome. One example of such avariable which can not generally be altered (e.g., targeted) to affectoutcome may be age and another such variable feature may be gender.

Thus, example embodiments of the described technology may have a numberof advantages, which can include among other things:

-   (i) Providing a more accurate prognosis for an individual input    vector (a personal outcome) when compared with the use of already    created local and global models;-   (ii) Providing a unique personal profiling methodology and system    and assisting with the design of possible improvement scenarios if    necessary;-   (iii) Providing an improved personalised model, in advance, or, when    new feature variables for a person are available or new samples in    the data are made available;-   (iv) Providing a personalised model which can capture and explain,    for an input vector x, specific interactions between feature    variables that can provide a key for better personalised profiling    and outcome prediction;-   (v) Providing a methodology and system which can be applied to a    wide range of scenarios where prediction of outcome is useful;-   (vi) Providing an improved formula for calculating local error in    predictive data analysis models.-   (vii) Providing a procedure to select nearest neighbours of a vector    x from a given data set, that procedure takes into account already    defined personalised weights of importance for each variable.

BRIEF DESCRIPTION OF DRAWINGS

Further aspects of the described technology will become apparent fromthe following description which is given by way of example only and withreference to the accompanying drawings in which:

FIG. 1 shows a block diagram of the method which would be implemented bythe system.

FIG. 2 shows diagrammatically the key parameters which are optimised andutilised in the present invention.

FIG. 3 shows the selected neighbourhood area Dx of 50 samples around asample #180 (represented as a diamond) in the 3D space of the top threeranked variables V11, V10 and V49 (out of 14) for the best personalisedclassification model related to classifying samples belonging to twoclasses—rocks and mines, from a standard bench mark data set explainedin Example 2.

FIG. 4 a-h Personalized modeling demonstrated in Example 3 on renalfunction evaluation data (Marshal et al, 2005). A new sample x isdenoted by a green triangle and its nearest neighbours Dx—as circles.All other data from a data set of 584 data samples are shown as “+”sign. Vx=3; Kx=30. Staring with equal weighting of the three variables,at 8 iterations shown in (a) to (h) different neighborhood sets Dx areselected depending on different weights Wx calculated. A WWKNN model Mxis created and its local accuracy Ax is evaluated on the 30 samples inDx. The average local error is calculated and visualized as darkness ofthe filled neighboring samples (the lighter the color, the less theerror).

FIG. 5 a A sample x from the Lymphoma outcome prediction data set (Shippet al, 2002) shown with 26 neighboring samples in the 3D space of thetop 3 gene expression variables (see Example 4).

FIG. 5 b An improvement scenario for sample x from the Lymphoma outcomeprediction data set (Shipp et al, 2002) for which a fatal outcome(class 1) is predicted. The figure shows how much each feature variable(gene expression value) needs to be changed for this person to <<move>>to the average good outcome profile (see Example 4).

FIG. 6 Identified SNP regions (associated signals) for the Crohn'sdisease from the whole data set (wtccc.org) in the WTCCC project (seeExample 5) and their mapping on the chromosomes. One gene, related to aSNP region, is identified that may be used as a treatment or drug target(modified from R. Lea at al, 2009).

FIG. 7 A block diagram of a personalised modelling, profiling and riskanalysis system for SNPs DNA data sequence analysis, obtained as anapplication system from the general block diagram in FIG. 1 (see Example5).

FIG. 8 A three point cross over operation is shown, where the twoindividual cross over through exchanging their genes in 4 sections,based on usually randomly selected 3 points.

DETAILED DESCRIPTION OF CERTAIN INVENTIVE EMBODIMENTS

For ease of reference the described technology will now primarily bediscussed in relation to an implementation for personalised medicinehowever this should not be seen as limiting. The described technologyhas applications in information science, mathematical modelling,personalised medicine, personalised drug design, personalized fooddesign, profiling and prognostic systems for predicting outcomes, orevaluating risks, based on a dataset of information which includesinformation relating to past outcomes for a given scenario.

Thus, the described technology may be applied to a wide range ofdatasets for which there may be information relating to the compositionof different data elements together with information as to the knownoutcome for an individual data element or combination of elements inrelation to a scenario of interest.

An underlying philosophy behind the described technology is therealisation that every person is different, and therefore an individualideally needs their own personalised model and tailored treatment. Theimplementation of this philosophy has now become more of a reality giventhe fact that more and more individual data for a person, e.g., DNA,RNA, protein expression, clinical tests, age, gender, BMI, sociofactors, inheritance, foods and drugs intake, diseases, to name afew—are more readily obtainable nowadays, and are easily measurable andstorable in electronic data repositories for a lesser cost.

The described technology includes a method and a system for theselection and ranking of important personal variables Vx related to aninput vector x and a problem, for the selection of the most appropriatenumber of nearest neighbouring samples Kx and also the most appropriatenearest samples, for the creation of an optimal personalised prognosticmodel Mx. The described technology allows for the prediction of outcome,or for risk evaluation, in relation to an input vector x following thecreation of the prognostic model Mx. The described technology can alsobe used for the design of personal improvement scenarios. The method ofthe described technology may be based on the use of a person'sinformation x, that may include DNA, gene expression, clinical,demographic, cognitive, psychiatric data, and a comparison against thispersonal information from other people within a data set. The proposedgeneral method iteratively selects the most important features(variables) Vx, ranks them through a weight vector Wx for the person inrelation to the problem, selects the optimum number Kx of neighbours andselects the set Dx of neighbouring samples, creates a personalisedprognostic model Mx with optimal parameters Px using the selectedvariables and nearest samples. These parameters, Vx, Wx, Kx, Dx, Mx, Pxmay be selected and optimised together, (e.g., in concert), so that thebest accuracy of the personalised prognosis, or close to it may beachieved. This is a desirable aspect of the proposed method. Anotherdesirable aspect of the method is a personalised profiling procedure interms of defining variables that may need to be modified in a concertfor the design of personal improvement scenarios afterwards, dependingon the problem and the available resources. The method allows for anadaptation, monitoring and improvement of the personalised model shouldnew data about the person or the population become available. Potentialapplications are in personalised medicine and personalised drug designfor known diseases, (incl. cancer, cardio-vascular disease, diabetes,renal diseases, brain disease, etc.), as well as for some othermodelling problems in ecology, meteorology, sociology, crime prevention,business, finance, to name but a few.

All references, including any patents or patent applications cited inthis specification are hereby incorporated by reference. No admission ismade that any reference constitutes prior art. The discussion of thereferences states what their authors assert, and the applicants reservethe right to challenge the accuracy and pertinence of the citeddocuments. It will be clearly understood that, although a number ofprior art publications are referred to herein, these references do notconstitute an admission that any of these documents form part of thecommon general knowledge in the art, in New Zealand or in any othercountry.

It is acknowledged that the term ‘comprise’ may, under varyingjurisdictions, be attributed with either an exclusive or an inclusivemeaning. For the purpose of this specification, and unless otherwisenoted, the term ‘comprise’ shall have an inclusive meaning—e.g., that itwill be taken to mean an inclusion of not only the listed components itdirectly references, but also other non-specified components orelements. This rationale will also be used when the term ‘comprised’ or‘comprising’ is used in relation to one or more steps in a method orprocess.

It is an object of the described technology to address the foregoingproblems or at least to provide the public with a useful choice.

Further aspects and advantages of the described technology will becomeapparent from the ensuing description which is given by way of exampleonly.

FIG. 1 diagrammatically details the key method elements a)-i) inrelation to a global data set D (1) relating to a scenario of interestand an input vector x (2) having a number of variables (3).

As per method elements a)-d) determine Vx and Kx then select theneighbourhood (4) then select, rank and optimize the most importantvariables Vx for a given individual input vector x and obtain a weightvector Wx of variable importance (5). Initially Vx variables may betreated as being equally important; however, in subsequent iterations ofthe method element s, the weighting vector Wx for each variable may berecalculated and optimized at element d). In addition subsequentinterations of the method element s a)-d) seek to optimize Vx, Kx, Wxand the neighbourhood Dx. Furthermore, in some preferred embodimentssubsequent iterations of element e) may also seek to optimize theclassification method used in the model Mx (6).

The creation of an optimized personalised model Mx for input vector x topredict the outcome of all samples from Dx, also involves an evaluationthe accuracy of the model through calculating a local error Ex of themodel Mx within Dx and the accuracy Ax is recalculated as part of theiterative application of the elements above.

In FIG. 2 all, or two or more of the parameters, Vx, Wx, Kx, Dx, Mx, Pxmay be selected and optimized individually or together, (e.g., inconcert), so that the best accuracy of prognosis, or a close to it, isachieved.

After finalising the model Mx, the output y=Mx(x) for the personal inputvector x is calculated (7), a profile of the individual represented by xin regard to possible outcomes is created. If necessary, improvementscenarios may be designed, consisting of suggested changes in the valuesof the selected personalised feature variables as a concert taking intoaccount their ranking, to improve the outcome.

The below Examples generally illustrate implementation of themethodology and systems of the present invention.

Example 1 Classifying Rock Versus Mine Based on the Reflection of SonarSignal

This may be a standard bench mark data set available from the Machinelearning repository of UC Irvine:

(http://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+%28Sonar%2C+Mines+vs.+Rocks%29).

The data set was contributed to the benchmark collection by TerrySejnowski, now at the Salk Institute and the University of California atSan Diego. The data set was developed in collaboration with R. PaulGorman of Allied-Signal Aerospace Technology Center.

The data contains 208 samples classified in two classes—rock vs minebased on 60 variables, continuous value between 0 and 1—reflections of asonar signal from the objects (mine or rock) in different frequencybands. The task is to classify any new input vector of 60 or less suchvariables into one of the two classes—rock or mine. While a globalapproach of using one neural network of the type of a multilayerperceptron and a backpropagation learning algorithm results in 85%accuracy of classifying new samples, here we demonstrate that theproposed personalized modeling method achieves 94% accuracy and revealsmore individual information about new objects.

To demonstrate the method a sample x may be randomly selected (this issample #180) and a personalized model is built to classify this samplefollowing the elements from the invention:

-   -   Element a) Vx=3 to 30.    -   Element b): Kx=20, . . . , 50.    -   Element c) A neighbourhood data set Dx of Kx samples is selected        from all 207 samples;    -   Element d): For each number of variables V=3 to 30 the variables        are weighted using a normalized SNR method to obtain the weight        vector Wx;    -   Element e): A model Mx is created using the WWKNN method, here        applied on a larger number of variables (60). The only parameter        P of the WWKNN model that can be optimized as part of the        optimization vector FIG. 2 is the classification threshold. Here        it is assumed to be fixed at 0.5.    -   Element f and g) The local accuracy is evaluated using formulas        (9-11) and stored.    -   Element h): The above elements are repeated in an exhaustive        search mode and the best model and its accuracy are recorded        which is given below:        Kx=50 neighbors;        Dx=(179 190 191 55 195 56 41 188 94 194 189 140 192 93 95 163        193 64 57 54 208 178 42 205 38 31 196 204 203 60 207 61 50 62 59        206 183 199 53 181 58 28 173 198 200 39 49 184 10 121)

The best local accuracy on training data is 94.00%.

The best selected number of variables is Vx=14, which are weighted using50 neighbouring samples of x. The neighbourhood area Dx is shown in FIG.3 in the space of the top three variables V11, V10 and V49.

Here is the weight vector Wx of the 14 features, evaluated using the SNRmethod (formula (8)) and then the SNR values are normalized across allfeatures (formula (5)):

Feature # Weighted SNR value 11 0.1048 10 0.0897 49 0.0878 48 0.0809 510.0769 36 0.0769 47 0.0746 12 0.0721 9 0.0679 35 0.0637 46 0.0580 280.0510 52 0.0483 27 0.0474Element i): Calculating the output y for x and profiling:Calculating the output for x:

sample # output predicted class actual class 180 1.64 2 2Profiling of sample 180 is done as explained in the description ofElement j of the invention, using formula (16):

Mean Value Mean Value Sample 180's Feature (Cls1) (Cls2) Value 11 0.17470.2896 0.3078 10 0.1593 0.2510 0.2558 49 0.0384 0.0637 0.0588 48 0.06950.1106 0.0969 51 0.0123 0.0194 0.0118 36 0.4607 0.3186 0.2897 47 0.09450.1469 0.0766 12 0.1916 0.3015 0.3404 9 0.1374 0.2135 0.1618 35 0.45550.3376 0.3108 46 0.1169 0.1988 0.0566 28 0.6731 0.7123 0.7834 52 0.01050.0160 0.0146 27 0.6877 0.7148 0.7373Weighted distance between sample 180 and the average class profiles foreach of the two classes is calculated using formula (18):Distance from class1 profile: 0.0744.Distance from Class2 profile: 0.0330.

The above distances show that sample 180 is closer to class 2 (a smallerdistance) and this is what was predicted above when the output wascalculated as 1.64.

Example 2 Personalised Modeling for the Evaluation (Prediction) of RenalFunction

In another implementation the method can be used for the evaluation ofthe level of function of a biological system or an organ of anindividual, such as the functioning of the heart, the kidney, etc. Thisis illustrated here on a case study problem of renal function evaluationbased on Glomerular Filtration Rate (GFR) as an accurate renalindicator.

Several nonlinear formulas have been used in practice as “goldenstandard” global models. The Gates formula (Gates, 1985) uses threevariables: age, gender, and serum creatinine, while the MDRD formula(Levey, 1995) uses six variables: age, sex, race, serum creatinine,serum albumin and blood urea nitrogen concentrations. While the existingformulas predict the GFR for patients from different geographic areaswith different accuracy, there is no systematic way to adapt theseformulas to new data and to personalize the prediction. The methodsuggested in (Marshal et al, 2005) is closest to this goal, but does nottake into account local weighting of the variables. The problem is ofprediction/identification as the output values are GFR continuousvalues.

Here the proposed in the invention method for personalized modeling isdemonstrated on the data from (Marshal et al, 2005). In FIGS. 4 a-h anew sample x is denoted by a triangle and its nearest neighbours Dx—ascircles. All other data from a data set of 584 data samples are shown as“+” sign.

For a chosen sample x (denoted as a diamond) only V=3 variables are used(Element a). A single value for nearest neighbors Kx=30 is used (Elementb). Starting with equal weighting of the three variables, differentneighborhood sets Dx are selected (Element c) depending on differentweights Wx calculated (Element d).

A WWKNN model Mx is created and its local accuracy Ax is evaluated onthe 30 samples in Dx (Element e) using formulas (8-11). In FIGS. 4 a-hthe average local error (formulas 9 and 10) is calculated and visualisedaccordingly at 8 consecutive iterations of: neighbourhood selection(Element c); variable weighting (Element d) and model creation (Elemente). At consecutive iterations different neighborhood areas Dx to thesample x are selected based on the previous local variable weighting Wx.Improved local accuracy Ax of the model Mx may be achieved through theseiterations. In the FIGS. 4 a-h the local error in the neighbourhood isshown as darkness of the filled neighboring samples (the lighter thecolor, the less the error).

The experiment here demonstrates that the proposed iterative nearestneighbor Dx selection based on iterative local variable weighting Wxleads to an improved result for an individual sample—the Root MeanSquare Error (RMSE) may be reduced more than twice (from 15.23 to 6.5)as shown below:

Variable V1 (Age) V2 (Screa) V3 (Surea) Sample x variable values: 0.12500.5881 0.8571 Variable weights Wx: Local error w1 w2 w3 (RMSE) FIG. 4a(Initial 1.0 1.0 1.0 15.23 model) FIG. 4h (final 0.1 0.8 0.1 6.5 model)

The above profile shows that variable V3 (urea) can be the mostimportant variable for the neighbourhood of the input sample x, followedby variable V2 (Serum creatinine). Using the calculated importancethrough an exhaustive search procedure leads to an improved prediction(a lower error of 6.5) in the neighbourhood of x. In the initial model,all 3 variables were assumed to have the same importance of 1 and thelocal error was more than 2 times higher (15.23).

Example 3 Personalised Modeling for Longevity Prediction of DialysisPatients

In another implementation, the described technology can be used topredict the longevity of a person, based on available data on thelongevity of other individuals under similar conditions.

This is illustrated on a case study example of longevity predictionafter haemodialysis using the well established DOPPS data as explainedbelow.

A medical dataset is used here for experimental analysis. Dataoriginates from the Dialysis Outcomes and Practice Patterns Study(DOPPS, www.dopps.org)—see also: D. A. Goodkin, D. L. Mapes & P. J.Held, “The dialysis outcomes and practice patterns study (DOPPS): howcan we improve the care of hemodialysis patients?” Seminars in Dialysis,Vol. 14, pp. 157-159, 2001.

The DOPPS is based upon the prospective collection of observationallongitudinal data from a stratified random sample of haemodialysispatients from the United States, 8 European countries (United Kingdom,France, Germany, Italy, Spain, Belgium, Netherlands, and Sweden), Japan,Australia and New Zealand. There have been two phases of data collectionsince 1996, and a third phase is currently just beginning. To date,27,880 incident and prevalent patients (approximately 33% and 66%respectively) have been enrolled in the study, which representsapproximately 75% of the world's haemodialysis patients. In this study,prevalent patients are defined as those patients who had receivedmaintenance hemodialysis prior to the study period, while incidentpatients are those who had not previously received maintenancehaemodialysis.

The research plan of the DOPPS is to assess the relationship betweenhaemodialysis treatment practices and patient outcomes. Detailedpractice pattern data, demographics, cause of end-stage renal disease,medical and psychosocial history, and laboratory data are collected atenrollment and at regular intervals during the study period. Patientoutcomes studied include mortality, frequency of hospitalisation,vascular access, and quality of life. The DOPPS aims to measure how agiven practice changes patient outcomes, and also determine whetherthere may be any relationship amongst these outcomes, for the eventualpurpose of improving treatments and survival of patients onhaemodialysis.

The dataset for the case study here contains 6100 samples from the DOPPSphase 1 in the United States, collected from 1996-1999. Each recordincludes 24 patient- and treatment related variables (features):demographics (age, sex, race), psychosocial characteristics (mobility,summary physical and mental component scores (sMCS, sPCS) using theKidney Disease Quality of Life (KD-QOL®) Instrument), co-morbid medicalconditions (diabetes, angina, myocardial infarction, congestive heartfailure, left ventricular hypertrophy, peripheral vascular disease,cerebrovascular disease, hypertension, body mass index), laboratoryresults (serum creatinine, calcium, phosphate, albumin, hemoglobin),haemodialysis treatment parameters (Kt/V, haemodialysis angioaccesstype, haemodialyser flux), and vintage (years on haemodialysis at thecommencement of the DOPPS). The output is survival at 2.5 years fromstudy enrollment (yes or no).

Several global-, local and transductive modeling techniques have beenapplied to the DOPPS data to create an accurate classification system.Unfortunately the best models published so far achieve only 74% accurateprediction (for a comparative analysis of different methods, see: Ma, QSong, M. R. Marshall, N Kasabov, TWNFC-Transductive Neural-FuzzyClassifier with Weighted Data Normalization and Its Application inMedicine, CIMCA 2005, Austria

The application of the method of the described technology leads to asignificant improvement of the accuracy and to a personalised modelderived that can be used to design a specific treatment for a person.

In relation to the experiment below (to test the method of the presentinvention) the number of training samples is 958 and initial number offeatures V=24. The classification method, to be utilized by the methodof the described technology is WWKNN with a fixed classificationthreshold of 0.5.

The number of neighbouring samples is Kx=50. Sample #5 is taken as a newsample for which a personlised model is developed and tested giving 84%local accuracy of prediction.

After several iterations according to the proposed method, the followingbest parameters and model are obtained:

-   -   Vx=2 (features 3 and 13);    -   SNR normalised weights Wx (formulas 8 and 5):        -   feature 3: 0.5254;        -   feature 13: 0.4746    -   Kx=50;    -   Dx=(455 405 300 107 451 576 78 895 589 612 77 725 207 705 44 529        160 605 444 869 43 48 348 83 331 356 846 238 97 278 882 894 484        79 447 68 526 42 525 179 50 415 718 195 210 240 298 118 766 664        180 121 410 411 108 786 81 788 499 787 672 631 905 872 407 886        881 237 62 889 239 586 206 396 915 952 320 891 867 104 722 393        35 893 443 523 857 34 771 476 372 865 609 52 26 395 658 38 687        151 851 126 432 798 321 712 453 618 211).    -   An WWKNN model is created. Best local accuracy on training data,        calculated using formulas (9-10), is 84.40%.    -   The output y of sample #5 is calculated using formula (11):

sample output actual class predicted class 5 1.99 2 2

-   -   Personalised profiling is performed for sample #5 (Element i)        from the invention using formula (16):

Feature Mean Value(Cls1) Mean Value(Cls2) Sample 5's Value 3 0.51610.5529 0.0000 13 0.4332 0.4557 0.5000

Weighted distance between sample 5 and the average class profile iscalculated using formula (18):

Cls1 Cls2 0.3029 0.3115

-   -   An improvement scenario (Element j) is designed via elements        (I)-(III) of the described technology using formulas (19) to        (21):

The sample x (sample 5 from the data base), for which a personalisedmodel is created, is predicted to be of class 2 (bad outcome). Apossible scenario for the person to become of class 1 (good outcome) maybe designed based on the changes in the two selected variables (3 and13) for the current person's values to the average values of the personsin the neighbourhood Dx of x who belong to class 1 (the good outcome):

Var- Person Impor- iable 5 values Average_class_1_values Desired_changestance  3 0.0000 0.5161 0.5161 0.5254 13 0.5000 0.4332 −0.0668 0.4746

Example 4 Feature Selection and Personalised Modeling for DiseaseOutcome Prediction Based on Gene Expression and Other Data

In one implementation, the proposed method and system may be applied forpredicting the outcome of a disease, such as cancer, based on geneexpression, protein and/or clinical data.

To illustrate this claim we use a case study problem and a publiclyavailable data set from Bioinformatics—the DLBCL lymphoma data set forpredicting survival outcome over 5 years period. This data set contains58 vectors—32 cured DLBCL lymphoma disease cases, and 26—fatal (seeShipp, M. A., K. N. Ross, et al. (2002). “Supplementary Information forDiffuse large B-cell lymphoma outcome prediction by gene-expressionprofiling and supervised machine learning.” Nature Medicine 8(1): 68-74.There are 6,430 gene expression variables. Clinical data is alsoavailable for 56 of the patients represented as IPI—an InternationalPrognostic Index, which is an integrated number representing overalleffect of several clinical variables.

The task is to:

-   -   (1) Create a personalised prognostic system that predicts the        survival outcome of a new patient x for whom same gene        expression variables are available.    -   (2) To design a personalised profile for x that can be used to        provide an explanation for the prognosis and design of        treatment;    -   (3) To find markers (genes) that can be used for the design of        new drugs to cure the disease or for an early diagnosis.

This data has been first published in (Shipp et al, 2002) where aleave-one-out un-biased cross validation modeling was performed. Forevery sample, a set of features was selected from the rest 57 samplesusing the signal-to-noise ratio, a model was created and tested on thissample with an overall accuracy of 70%. Here, using the same data andthe same cross validation un-biased procedure as detailed in (Shipp etal, 2002), but applying the method of the present invention, an overallaccuracy close to 90% may be achieved. As an illustration, here apersonalized model for sample #34 may be created using: 57 samples fromthe data set, each of them described as a vector of 6430 variables(genes)

A WWKNN model, with a threshold of 0.5, was derived and a profile of thesample 34 was created along with an improvement scenario as sample #34was correctly predicted by the created model to belong to the class ofthe fatal outcome.

After the iterative parameter optimization in Elements a-h) thefollowing model Mx may be created.

-   -   Kx=26 neighbours of sample 34;    -   Neighbouring area Dx=(24 44 39 29 56 31 52 1 20 55 47 49 40 25        17 18 16 57 46 48 23 42 6 3 50 41);    -   5 features are selected as optimal for sample 34 and weighted        through SNR for the area Dx (formulas 8 and 5):

Feature (gene) Weighted SNR value 2915 0.2182 3513 0.2091 5460 0.19154533 0.1910 5423 0.1902

FIG. 5 a shows the 26 samples from the Dx in the 3D space of the topthree variables only (genes #2915, 3513, 5460).

-   -   A WWKNN model may be created and tested as per Element e. The        best local accuracy Ax in Dx on the 26 data samples, calculated        using formulas (9-10) is 80%.

The calculated output for sample 34 using formula (11) is 0.59 and asthe classification threshold is 0.5, sample 34 is classified to belongto class 1 (bad outcome, output value 1)).

A profiling of sample 34 is designed using formula (16):

Profiling: Feature Mean Value(Cls0) Mean Value(Cls1) Sample 34's Value2915 166.5706 37.4990 20.0000 3513 50.9251 187.9606 201.7022 546020.0000 35.5601 20.0000 4533 198.5793 48.7171 20.0000 5423 43.768421.2006 20.0000

A weighted distance between sample 34 and the average class profiles forClass 0 (good outcome) and Class 1 (fatal outcome) is calculated usingformula (18) as:

-   -   102.1396 (for class 0)    -   15.3837 (for class 1).

The above distances show that sample 34 is closer to the average profileof the fatal outcome (class 1) than to the good outcome (class 0) thatis also confirmed by the predicted above output value of 0.59 for sample34.

A scenario for the improvement of a person 34 in terms of requiredchanges in the gene expression values of each feature variable (gene)according to Element J from the invention (formulas 19-21) is shownbelow and illustrated in FIG. 5 b:

Desired Im- Gene Actual_Value Desired_aver_profile Improvem. portance2915 20.0000 166.5706 146.5706 0.2182 3513 201.7022 50.9251 −150.77710.2091 5460 20.0000 20.0000 0.0000 0.1915 4533 20.0000 198.5793 178.57930.1910 5423 20.0000 43.7684 23.7684 0.1902

The above improvement scenario can be interpreted in the following way:In order to improve the outcome for person #34 towards the good outcome(survival), some genes (proteins) need to change their expressionsthrough drug intervention or other means, so that: genes 2915, 4533 and5423 are stimulated for a higher expression; gene 3513 is suppressed fora lower expression; and gene 5460 is unchanged. This interpretation hasthe potential to be used for a personalized treatment (e.g. drug) designfor this person, where only genes 2915, 4533, 5423 and 3513 are affectedby the treatment, also taking into account their importance, defined asa local weight in the neighborhood Dx.

After a certain period of treatment, a new model and a new profile forthis person, based on a new input vector x′ can be derived, using thesame invention, and the previous treatment modified accordingly, untilthis person is cured.

Aspects of the described technology have been described by way ofexamples only and it should be appreciated that modifications andadditions may be made thereto without departing from the scope of theappended claims.

Example 5 Personalised Modelling for Risk of Disease Evaluation,Diagnosis, Treatment and Drug Design Using DNA SNP Sequence Data

An individual DNA sequence, that can be obtained from any cell of aliving organism (e.g. human, animal, plant, virus) carries not only theinherited traits or risk of diseases through generations, but also showsthe current state of the organism in terms of accumulated mutationsduring life time. This information can be used to predict the uniquepersonalized trait of the organism, risk of disease or diagnosis, at thetime of the DNA sequencing subject to sufficient data samples ofmeasured DNA and their traits. Collecting individual DNA sequence dataand measuring Single Nucleotide Polymorphisms (SNP) (eg A to A, A to G,G to G) for an individual and a large population of individuals becomeseasy and cheap with the advancement of the microarray technologies.

Such data has been collected and published as part of Genome-wideAssociation Scan projects (GWAS). Results of 374 such projects for over100 human traits and diseases are published in (Hindorff L A, SethupathyP, Junkins H A, Ramos E M, Mehta J P, Collins F S, and Manolio T A.Potential etiologic and functional implications of genome-wideassociation loci for human diseases and traits. Proc Natl Acad Sci USA,May 27, 2009) and the collected data is available on the Internet. SuchGWAS project is also the WTCCC project in the UK, results published inNature, 2007 (The Welcome Trust Case Control Consortium, Genome-wideassociation study of 14,000 cases of seven common diseases and 3,000shared controls, Nature, vol. 447, 2007, 661-670) and obtained dataavailable from the Internet. The publications so far report on thestatistically derived population risk of disease (trait) for single SNPssignals (see also: Lea, Rod, Donia Macartney-Coxson, David Hall, BushraNasir and Lyn Griffiths, A Novel Bioinformatic Approach for IdentifyingGenomic Signatures of Disease Risk, ESR Ltd Report, Porirua, NewZealand, 2009). The challenge is to use the available SNP data to derivea personalized risk and SNP (gene) signature for a new individual, alongwith possible treatment and drug design, that take into account thespecific interaction and combination of several SNPs specific for thisperson.

We claim that the proposed in the patent specification method isapplicable to SNP data (e.g. wtccc.org) to obtain an individual SNPsignature for every new person and predict the individual risk of thisperson for the following diseases included in the UK WTCCC study, asillustrated later in this Example:

-   -   Bipolar disorder;    -   Coronary artery disease;    -   Crohn's disease;    -   Hypertension;    -   Rheumatoid arthritis    -   Type 1 Diabetes;    -   Type 2 Diabetes;    -   Tuberculosis;    -   Breast cancer;    -   Multiple sclerosis;    -   Ankylosing spondylitis;    -   Autoimmune thyroid disease as well as to predict the individual        risk or diagnosis for other diseases based on data collected and        published elsewhere including brain injury and brain        degenerative diseases, such as:    -   Stroke;    -   Alzheimer disease;    -   Mental retardation;    -   Schizophrenia    -   and many more

Here we describe how the proposed methodology can be applied to SNPdata, exemplified on the WTCCC data for any of the diseases above. Wetake as a concrete example the Crohn's disease.

First, based on the SNPs data of diseased and control persons, uniqueSNPs for the diseased when compared to the controls are statisticallyidentified using the method from (Nature, 2007)). An example ofidentified 9 SNP association signals for the Crohn's disease in theWTCCC project, across all chromosomes, is given in FIG. 6. Each SNPassociation signal (area from the DNA) may contain several SNPs and ispart of a gene that could be a possible target for a treatment or drugdesign.

The methodology of the described technology may applied here on a dataset D of samples (both controls and diseased) that contain selected SNPsto create a personalized model for a new person, represented as inputvector x containing the same SNPs, for the prognosis of the risk of thisperson of the disease (or a trait under consideration) and to create aSNP and gene signature of the person for a possible treatment or a drugdesign. The following elements are realized iteratively (as also shownin the block diagram of FIG. 7 which is derived from the general blockdiagram in FIG. 1).

According to another aspect of the described technology method ofcreating an optimised personalized model of a person's medical conditionbased on an analysis of selected SNPs is provided comprising:

-   -   (a) determining a number of SNP variables and selecting a subset        of SNPs variables Vx from a dataset D;    -   (b) determining a number Kx of nearest neighbors of SNP vectors        to x from D;    -   (c) selecting a subset Dx from the set D of Kx neighboring        samples to x according to the set of SNP variables Vx;    -   (d) ranking the SNP variables from Vx according to their        discriminative power in Dx, e.g. to discriminate controls versus        diseased in Dx;    -   (e) creating a personalised prognostic model Mx for the risk of        disease of person x (e.g. a linear regression, a neural network        or else) with parameters Px, using the selected variables Vx and        nearest samples in Dx.    -   (f) testing the predicted by Mx risk for every sample from Dx        and compare it to the known risk calculating the average local        error Ax across all samples from Dx.    -   (g) storing all parameters and values from the above        elements (a) to (f) as results of the current iteration.    -   (h) repeating elements (a) to (g) until the best local accuracy        is achieved.

According to another aspect of the described technology a method ofcalculating the risk of disease from an optimized SNP model of a subjectderived substantially as described above is provided, the methodcomprising:

-   -   (i) using the model Mx derived from the above iterations to        calculate the risk y for the sample x.    -   (ii) creating a SNP profile of x and the corresponding gene        profile by mapping the SNPs from the final set Vx into genes as        illustrated in FIG. 6.    -   (iii) creating a scenario for treatment/drug design that        includes a set of

SNPs/genes and the needed changes for the person x to match in thefuture the average profile of the control samples from Dx.

The data sub-sets and parameters Vx, Wx, Kx, Dx, Mx, Px are selected andoptimised together through several iterations of the procedure above asdescribed in the method, so that the best accuracy of the personalisedprognosis, or close to it is achieved as a target/objective function.The method allows for a dynamic adaptation, monitoring and improvementof the personalised model should new data about the person or thepopulation become available over a time period. For example, in someyears time aging and environmental factors (radiation, nutrition,smoking, drugs, etc) might have made impact on the person's DNA and newrisk evaluation would be needed when possibly new known samples will beadded to the data set D.

To illustrate the use of the proposed methodology on SNP data fordisease risk prognosis, we will use a subset of 1048 samples from theWTCCC data repository related to both control subjects (488, no disease)and Crohn's diseased subjects (560) for which already 53 SNPs areidentified as statistically significant for the whole population at apre-processing stage. The data was kindly provided by Dr Rod Lea fromthe Environmental Science Research Ltd, CRI, New Zealand.

We will show here the development of a personalised model for theprediction of Chron's disease of a new subject (input vector x).

After several iterations of a genetic algorithm (GA) optimisationprocedure to optimise together features, number of nearest samples andmodel parameters, the following results were obtained as the bestresults for the sample x:

-   -   (a) The number of SNPs Vx that best predicts the outcome for x        is 10 and the SNP features are the following ones (out of 53):        40, 10, 19, 42, 21, 34, 45, 49, 30, 22.    -   (b) The optimal number Kx of nearest samples is 67 (out of 1048        total number of samples).    -   (c) The nearest samples selected are #: 647 742 458 255 258 513        697 245 486 728 823 920 1035 24 140 144 394 581 612 710 775 907        910 916 1027 131 273 336 585 635 646 672 699 763 812 816 819 849        958 1013 56 165 210 226 246 266 272 557 575 576 671 724 735 752        754 770 800 871 884 934 952 966 981 1032 44 52 61    -   (d) A weight vector for the Vx variables is obtained.    -   (e) A WKNN model is used for classification with an optimised        parameter—a class decision threshold of 0.19.    -   (f) The local accuracy is evaluated as 85% correct.    -   (g) The kNN model is used to calculate the output risk for x as        y=0.57. As this sample was with a known outcome to be 1        (diseased) the peronsalised model correctly predicted this        outcome (using an optimised threshold of 0.19).    -   (h) A personalised SNP signature for x is developed based on the        22 control samples in the neighbourhood of 97 samples and 45        diseased. The local probability of each of the three SNPs        denoted as 0, 1 and 2 in each of the controls and diseased        samples of the neighbourhood of x are the following:

Control (22) Diseased (45) Actual Value of SNP ID 0 1 2 0 1 2 the SNP inx 40 0.55 0.45 0.00 0.38 0.58 0.04 1 10 0.64 0.36 0.00 0.76 0.24 0.00 019 0.77 0.23 0.00 0.67 0.22 0.11 0 42 0.73 0.18 0.09 0.71 0.24 0.04 0 210.36 0.50 0.14 0.31 0.58 0.11 1 34 0.64 0.36 0.00 0.80 0.20 0.00 0 450.32 0.68 0.00 0.36 0.51 0.13 1 49 0.59 0.41 0.00 0.58 0.42 0.00 1 300.09 0.50 0.41 0.16 0.80 0.24 2 22 0.50 0.50 0.00 0.56 0.44 0.00 0

It is seen from the above table that SNP features #40, 10 and 34 areprominent in the diseased group versus the control group in theneighbourhood of x. These SNPs may be mapped into genes and thenexplored as possible drug or treatment targets.

Aspects of the described technology have been described by way ofexample only and it should be appreciated that modifications andadditions may be made thereto without departing from the scope thereof.

REFERENCES General References and Citations

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What I claim is:
 1. A method of decreasing a risk of disease in a person x, comprising: (A) obtaining a single nucleotide polymorphism (SNP) transductive model Mx suitable for use in data analysis, wherein the risk of disease specific to the person x is represented as input vector x, which comprises a plurality of variable features in relation to the risk of disease for which there is a global dataset D of samples also having the same variable features relating to the risk of disease as input vector x, and for which an outcome is known, (B) optimizing the transductive model by a) determining what number and a subset Vx of variable features of input vector x will be used in assessing an outcome for the input vector x; b) determining what number Kx of samples from within the global data set D will form a neighborhood about input vector x; c) selecting suitable Kx samples from the global data set which have the variable features that most closely accord to the variable features of the person x to form the neighborhood Dx; d) ranking the Vx variable features within the neighborhood Dx in order of importance to the outcome and obtaining a weight vector Wx for all variable features Vx; e) creating a prognostic transductive model Mx for each input vector x, having a set of model parameters Px and the other parameters Vx and Kx from elements a)-d); f) testing an accuracy of the model Mx for each sample from Dx by a method selected from the group consisting of: (i) calculating Wx as normalized SNR (Signal-to-Noise Ratio) coefficients and sorting the variables in descending order: V1, V2, . . . , Vv, where: w₁>=w₂>= . . . >=w_(v), calculated as follows: w _(l)=abs(M _(l) ^((class 1,x)) −M _(l) ^((class 2,x)))/(Std_(l) ^((class1))+Std_(l) ^(class2))); (ii) testing for a plurality of variables Vx a plurality of possible combinations of values of their weights Wx tested through a search to increase the overall accuracy of a model built on the data Dx; (iii) applying a genetic statistical analysis procedure, if the number of variables prevents using method (ii) above; (iv) applying a quantum inspired evolutionary statistical analysis technique, to select the optimal variable set Vx for every new input vector x and to weigh the variables through a probability wave function; g) storing both the accuracy and the set of model parameters; h) repeating elements a) and/or b) while applying an optimization procedure to optimize Vx and/or Kx, to determine their optimal values, before repeating elements c)-h) until the accuracy is maximized, wherein a number and a subset Vx of variable features of input vector x, and a number Kx of samples from within the global data set D that form a neighborhood about input vector x are determined anew each time elements a) and b) are repeated while applying an optimization procedure to optimize Vx and/or Kx; (C) creating a SNP profile of sample x from patient x and a corresponding gene profile by mapping the SNPs from a final set Vx into genes; (D) determining the risk of disease specific to the patient x using the optimized transductive model Mx by: (I) forming a vector: Fx={Vx,Wx,Kx,Dx,Mx,Px,t}, where the variable t represents the time of the model Mx creation; (II) calculating the weighted distance D(Fx,Fd) as an aggregated indication of how much a person's profile should change to reach an average desired profile Fd: D(Fx,Fd)=Σ_(l=1,v)abs(V _(lx) −V _(ld))·w _(l); (III) designing a vector of required variable changes, defined as: deltaFx,d=(deltaV _(lx,d))_(,for l−1,v) as follows:  (20) deltaV _(lx,d) =V _(lx) −V _(ld), with an importance of: w_(l)  (21) (E) modifying variable features Vx in the patient x to be closer to Kx values associated with an improved outcome relative to a prognostic outcome y determined for the patient x so as to improve the prognostic outcome of the patient x; (F) repeating elements a) through h) to determine an improved prognostic outcome using re-optimized transductive model Mx; and (G) creating a scenario for treatment/drug design that includes a set of SNPs/genes and required changes for the person x to match in future, average profiles of control samples from Dx in order to decrease the risk of disease.
 2. The method as claimed in claim 1, wherein optimizing the transductive model further comprises profiling input vector x and comparing important variable features against important variable features associated with a desired outcome to provide for, or assist with, development of scenarios for improvement of the outcome for input vector x.
 3. The method as claimed in claim 1, wherein the prognostic transductive model Mx is a personalized model.
 4. The method as claimed in claim 3, wherein the personalized model is a unique personalized model.
 5. The method as claimed in claim 1, wherein a known outcome is associated with each sample in the global dataset and determined neighborhood.
 6. The method as claimed in claim 1, wherein the global dataset has samples having one of at least two different outcomes, wherein a particular outcome for each sample is known.
 7. The method as claimed in claim 1, wherein new data is compared with accumulated existing data samples for which a future outcome is known for each sample.
 8. The method as claimed in claim 1, wherein one or more variable features of input vector x are selected as incapable of being altered for step (D)(III).
 9. The method as claimed in claim 1, wherein step (E) comprises administration of a drug.
 10. A computer system which includes: a hardware comprising, a processor and associated memory for performing the method of claim
 1. 11. A non-transitory computer readable medium which contains a program executed by a processor for performing a method, the method comprising: (A) obtaining a single nucleotide polymorphism (SNP) transductive model Mx suitable for use in data analysis, wherein the risk of disease specific to the person x is represented as input vector x, which comprises a plurality of variable features in relation to the risk of disease for which there is a global dataset D of samples also having the same variable features relating to the risk of disease as input vector x, and for which an outcome is known, (B) optimizing the transductive model by: a) determining what number and a subset Vx of variable features of input vector x will be used in assessing an outcome for the input vector x; b) determining what number Kx of samples from within the global data set D will form a neighborhood about input vector x; c) selecting suitable Kx samples from the global data set which have the variable features that most closely accord to the variable features of the person x to form the neighborhood Dx; d) ranking the Vx variable features within the neighborhood Dx in order of importance to the outcome and obtaining a weight vector Wx for all variable features Vx; e) creating a prognostic transductive model Mx for each input vector x, having a set of model parameters Px and the other parameters Vx and Kx from elements a)-d); f) testing an accuracy of the model Mx for each sample from Dx by a method selected from the group consisting of: (i) calculating Wx as normalized SNR (Signal-to-Noise Ratio) coefficients and sorting the variables in descending order: V1, V2, . . . , Vv, where: w1>=w2>, . . . >=wy, calculated as follows: w ₁=abs(M1^((class 1,x)) −M1^((class 2,x)))/(Std1^((class1)) +Std1^((class 2))); (ii) testing for a plurality of variables Vx a plurality of possible combinations of values of their weights Wx tested through a search to increase the overall accuracy of a model built on the data Dx; (iii) applying a genetic statistical analysis procedure, if the number of variables prevents using method (ii) above; (iv) applying a quantum inspired evolutionary statistical analysis technique, to select the optimal variable set Vx for every new input vector x and to weigh the variables through a probability wave function; g) storing both the accuracy and the set of model parameters; h) repeating elements a) and/or b) while applying an optimization procedure to optimize Vx and Kx, to determine their optimal values, before repeating elements c)-h) until the accuracy is maximized, wherein a number and a subset Vx of variable features of input vector x, and a number Kx of samples from within the global data set D that form a neighborhood about input vector x are determined anew each time elements a) and b) are repeated while applying an optimization procedure to optimize Vx or Kx; (C) creating a SNP profile of sample x from person x and a corresponding gene profile by mapping the SNPs from a final set Vx into genes; (D) determining a prognostic outcome y specific to the person x using the optimized transductive model Mx by: (I) forming a vector: Fx={Vx,Wx,Kx,Dx,Mx,Px,t}, where the variable t represents the time of the model Mx creation; (II) calculating the weighted distance D(Fx,Fd) as an aggregated indication of how much a person's profile should change to reach an average desired profile Fd by using the following: D(Fx,Fd)=Σ_(l=1,v)abs(V _(lx) −V _(ld))·w _(l); (III) designing a vector of required variable changes, defined as: deltaFx,d=(deltaV_(lx,d)), for l=1, v as follows: deltaV_(lx,d)=V_(lx)−V_(ld), with an importance of: Wl; (E) modifying variable features Vx in the person x to be closer to Kx values associated with an improved outcome relative to the prognostic outcome y determined for the person x so as to improve the prognostic outcome of the person x; (F) repeating elements a) through h) to determine an improved prognostic outcome using re-optimized transductive model Mx; and (G) creating a scenario for treatment/drug design that includes a set of SNPs/genes and required changes for the person x to match in future, average profiles of control samples from Dx in order to decrease the risk of disease. 